Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…</p><h3>Methods</h3><p dir="ltr">Using Python® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. …”

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques by Umesh Kumar Lilhore (17727684)

“…The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F 1 – Score , and recall. …”

Arabic Educational Neural Network Chatbot by A. Alazzam, Bayan

“…To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. …”
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A study on Speaker Recognition System by Bakkar, Hazem Wa'il Mohammed

“…The proposed system was mainly developed using Python (Python.org, 2015). This system was used to implement and study several methods and techniques in speaker recognition domain. …”
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Investigation of Forming a Framework to shortlist contractors in the tendering phase by DABASH, MOHANNAD SALAH

“…The aim of this research is to create a framework that can predict the best contractor to be awarded a construction contract by a consultant/client using a different set of variables known as “Decision factors.” …”
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KNNOR: An oversampling technique for imbalanced datasets by Ashhadul Islam (16869981)

“…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. …”